The automated processing of information has been an enormous benefit to businesses because it has greatly increased the effectiveness and efficiency of decision makers at every point in a decision path. Every enterprise regardless of whether it is a government, commercial business or not-for-profit organization has the operational necessity to manage information.
This information is used to treat patients, acquire customers, input orders, ship product, bill customers, collect invoices, pay employees and vendors, order product, audit inventory and maintain records of transactions between employees, customers and suppliers, for example, in the case of a commercial business.
In the normal course of events, information is acquired, processed and consolidated utilizing software, computer hardware and digital networks in accordance with each organization's internal operational model. Unfortunately, the automated processing of information is fraught with many debilitating problems preventing the useable, timely, and cost effective integration, standardization, and reporting of data.
One previous approach focused on constructing enterprise data warehouses to collect consolidated and standardized data from an entire organization. The typical enterprise data warehouse requires operational data from many sources to be extracted, transformed, and loaded into a third normal form Operational Data Store database which is again extracted, transformed, and loaded into a star and snowflake data vault database. The data vault database can then be loaded into data marts, each dedicated to a particular department or function.
Each database in the enterprise data warehouse formation and functioning process must be designed, maintained, and populated with a custom Extract Transform Load (ETL) function. Furthermore, all stages in the development and use must be completed, in some form, before the organization is able to generate reports and begin to realize benefits from the enterprise data warehouse.
While an enterprise data warehouse achieves standardized data that is centrally managed for an entire organization, this comes at a very high cost. The resources required to implement a comprehensive enterprise data warehouse can be prohibitive to all but a very select few, as monetary costs can be astronomical. Even when monetary resources are not the limiting factor, the time to build and implement an enterprise data warehouse is commonly measured in years.
Another shortcoming of enterprise data warehousing stems from the enterprise data warehouse focus on decision support applications, which emphasize summarized information. An inherent disadvantage to these systems is that transaction details about the customer's identity are lost. Enterprise data warehouses exhibit shortcomings when applied to applications such as customer data analysis. Customer data analysis is a decision support analysis that correlates data to customers' activities, events, transactions, status and the like. Summarized information usually loses the detail level of information about customer identity, limiting the usefulness of enterprise data warehousing approaches in these applications.
Other approaches focus on creating department focused data marts directly from an organizations operational data. Department focused data marts only need to incorporate the data relevant to a single department. Because of this, department focused data marts can be much smaller.
Due to the smaller size department focused data marts generally take fewer resources in terms of time and money to build; however, these benefits also come at a steep cost. Department focused data marts are not centrally managed and do not have consistent standards in terms of quality or data formats.
When department focused data marts are created, the inconsistent standards prevent integration across the organization. Also, since each department focused data mart is created without an overarching plan the total amount of resources invested for each department to have a data mart can be substantially higher than creating a single well planned enterprise data warehouse. The resources to maintain inconsistent department focused data marts can also be much higher than maintaining a single enterprise data warehouse.
Currently, there is no comprehensive solution that resolves the problem of providing useable, timely, and cost effective integration, standardization, and reporting of data. This need has been long felt in the industry.
Prior developments have not taught or suggested any solutions to overcome all of the limitations described above, and thus, solutions to overcome these limitations have long eluded those skilled in the art.